The present invention relates in general to cabin air filters for automotive vehicles, and, more specifically, to detecting when an air filter has become clogged and is in need of replacement.
Because of the potential for airborne contaminates, cabin air filters are often included in the ventilation systems of automotive vehicles. These replaceable filters need to be changed when they become clogged with filtered particles and dirt. Dedicated sensing systems are known that can identify the filter state and provide an indication to the user to let them know when an air filter needs to be replaced. The known prior art systems have used combinations of dedicated sensors (such as a pressure sensor for determining pressure drop across a filter) and special housing design features (such as an air bypass channel). Such systems have proven costly and consume packaging space that is in short supply. They also add complexity to the manufacturing of the automotive heating, ventilation, and air conditioning (HVAC) system. Consequently, many vehicles are manufactured without any automatic monitoring of the state of the air filter. Instead, the manufacturer provides written recommendations to the user to replace the filter after a certain period of time or after a predetermined number of miles driven. These recommendations are determined based upon average conditions, so any particular user may have a clogged filter before expiration of the recommended interval or they may end up replacing an unclogged filter unnecessarily. Therefore, it would desirable to monitor the state of an air filter without dedicated sensors or special housing features.
It has been proposed to use the increased resistance to air flow through a dirty air filter to detect the need for a filter change. For example, U.S. Pat. No. 6,448,896 to Bankus et al. issues a command to a blower motor for a particular speed. The resulting speed is compared to a predetermined fan speed that would normally occur when the filter is dirty. However, such prior systems have been unreliable and have been found to give inaccurate results due to failures to recognize and control all significant variables impacting the air flow.